GPU Memory Requirements for AI Training Vs Inference

AI Training vs. AI Inference: What Is the Difference?

Before estimating GPU memory, it is important to understand the difference between AI training and AI inference. They are two different stages of working with an AI model, and they can have very different GPU requirements.

What Is AI Training?

AI training is the process of teaching an AI model. The model is given large amounts of data and learns patterns from that data by adjusting its internal parameters, or weights.

For example, a company developing its own LLM might train the model on large collections of text so that it learns how to understand and generate language. Similarly, a computer-vision company might train a model using thousands or millions of images so it can recognize objects, defects or other visual patterns.

Training is generally compute- and memory-intensive because the GPU must perform calculations, store intermediate results, calculate gradients and update the model's parameters.

Businesses that may need AI training include:

• AI companies developing their own models

• Research organizations and universities developing new AI systems

• Large enterprises building or significantly customizing proprietary models

• Companies developing specialized computer-vision or machine-learning models

• Organizations performing extensive model fine-tuning

Most businesses, however, do not need to train a large AI model from scratch. They may instead use an existing model and focus on inference or, in some cases, fine-tuning.

What Is AI Inference?

AI inference is the process of using a trained AI model to produce an output.

When you ask a chatbot a question and it generates an answer, that is inference. When an AI system examines an image and identifies a defect, or when a recommendation engine predicts what a customer might want, the system is performing inference.

In simple terms: Training teaches the model. Inference uses the model.

Businesses commonly need inference when they are deploying AI applications into production, such as:

• AI chatbots and virtual assistants

• LLM-powered search and knowledge systems

• Customer-service automation

• Recommendation engines

• Fraud detection

• Document processing

• Computer-vision inspection

• Video analytics

• Generative AI applications

For many businesses adopting AI today, inference is likely to be more relevant than training. They may use an existing model from an AI provider, open-source model or internally developed model and need GPU infrastructure to run that model for employees or customers.

Why Does Training Usually Need More GPU Memory?

The difference becomes important when selecting a GPU.

During inference, the GPU primarily needs to store the model weights, along with memory for the KV cache, input data and other runtime requirements.

During training, the GPU needs to store the model weights plus additional information required to learn and update the model, including activations, gradients and optimizer states.

This means the same model can have dramatically different memory requirements depending on whether you are training it or simply running it.

There is also a middle ground: fine-tuning. A business may take an existing model and adapt it to its own data or requirements instead of training a completely new model. Depending on the fine-tuning method, this can require considerably less GPU memory than training a model from scratch.

The key takeaway for GPU buyers is simple:

If you are building or substantially training an AI model, plan for much higher GPU memory requirements. If you are deploying an existing model, your requirements will usually be driven by inference performance, model size, context length, concurrency and efficiency.

One of the most important differences between AI inference and AI training is how much GPU memory they require.

During inference, the GPU is primarily running an already-trained model. During training, the GPU is also storing information needed to learn and update the model. This creates a much larger memory requirement.

What uses GPU memory during inference?

When an LLM generates an answer, GPU memory is mainly used for:

• Model weights: The parameters of the trained model.

• KV cache: Information retained from previous tokens, especially important for longer conversations.

• Input and output data: The tokens being processed.

• Runtime overhead: Memory required by the AI framework and other operations.

For example, if a 7B model requires around 14GB to store its weights at FP16/BF16, that does not mean a 14GB GPU is automatically sufficient for inference. Additional memory is needed for the KV cache and other operations.

What changes during training?

Training requires the GPU to calculate how the model should change after processing each batch of data. To do this, it needs to keep several additional types of information in memory.

These include:

• Model weights: The current parameters of the model.

• Activations: Intermediate results produced while the model processes training data.

• Gradients: Information showing how the model's weights should be adjusted.

• Optimizer states: Additional values used by the training algorithm to update the weights.

The important point is that all of this information may need to reside in GPU memory while training is taking place.

As a simplified example, imagine a model whose weights require 20GB of GPU memory. During inference, you might need somewhat more than 20GB because of the KV cache and other overhead. During training, however, the GPU may need substantially more memory because it must also store activations, gradients and optimizer states.

This is why a GPU that can run an LLM for inference may not have enough memory to train the same model.

What about fine-tuning?

Fine-tuning falls somewhere between these scenarios, depending on the method used.

Full fine-tuning updates most or all of the model's parameters and can have substantial memory requirements. Parameter-efficient methods, such as LoRA and QLoRA, update a much smaller portion of the model and can significantly reduce the memory required for fine-tuning.

The exact memory requirement therefore depends on the model, precision, batch size, sequence length, training method and software configuration.

A simple way to remember the difference is: Inference mainly needs memory to run the model. Training needs memory to run the model and store the information required to update it.

This is why high-memory NVIDIA GPUs such as the A100 80GB and H200 can be useful for demanding AI training workloads, while smaller-memory GPUs may be sufficient for appropriately sized inference applications. 

Also read our guide: how much GPU memory is needed for AI LLMs

A Simple Real-World Comparison

Think of AI training like teaching a new employee, while AI inference is like that employee doing their job.

During training, the employee needs to study large amounts of information, take notes, practice, make mistakes and learn from those mistakes. An AI model similarly needs to process training data, calculate errors and adjust its parameters. This requires considerably more computational work and memory.

Once the employee has learned the job, they can perform it using what they have already learned. That is similar to AI inference: the trained model receives an input and produces an output without having to relearn the underlying task.

So, in simple terms: 

• AI training = teaching the model 

• AI inference = using the trained model

A business developing its own AI model may need substantial GPU infrastructure for training. A business deploying an existing LLM as a customer-service chatbot, internal assistant or document-processing system will generally need GPU infrastructure primarily for inference.